Abstract

Interactions among proteins mediate regulatory, mechanical, structural and transport functions and are fundamental in the
modulation of all cellular activities. About 15 years ago, it was first noted that physical interactions link most if not
all proteins to each other in complex networks. The analysis of maps representing current knowledge about these molecular
networks can provide insights that range from specific hypotheses on the function of a given protein to system‐level properties
on the organisation or evolution of biological systems. Here, we provide an updated introduction to the numerous ways and
available tools by which protein–protein interaction maps can be analysed, and caveats that need to be kept in mind.

Key Concepts

Protein–protein interaction maps are important tools to develop functional hypothesis, contextualise ‘omic’ gene lists, analyse
system properties and systems evolution and provide an important basis for analysing system dynamics.

Networks can be assembled from small‐scale literature data, bioinformatic prediction and experimental high‐throughput technologies
of which affinity‐purification followed by mass spectrometry or binary yeast‐based assays are most common.

Global and local properties of protein–protein interaction maps can be influenced by sociological, technological and experimental
biases that need to be taken into account.

High‐throughput technologies can provide data that are in quality at least comparable to the high‐quality fraction from small‐scale
studies in public databases.

Carefully benchmarked and appropriate quality controls must be implemented for all approaches.

Figure 1. Approaches to measure protein–protein interactions affect the type of detected interactions and bias the resulting network maps. Y2H assays identify predominantly direct binary interactions among two proteins X‐DB (DNA‐binding domain) and Y‐AD (activation domain) in an in vitro setting. Affinity purification followed by mass spectrometry (AP‐MS) allows selective capture of the target protein (e.g. Y‐TAP) and their directly (X) or indirectly associated partners (grey circles), which may or may not be in the same complex. In the constructed network, it is not possible to differentiate between direct or indirect associations. Literature‐curated networks contain interactions at different levels of documentation from both binary and protein complex‐derived methods. Popular proteins tend to be more studied and may have more interactions due to this social bias.

Figure 2. Strategies to analyse interaction network maps. Protein networks can be combined with additional information. Integration with functional annotations (left panel) may allow development of functional hypothesis for uncharacterised proteins (red nodes) interacting with several functionally characterised proteins (blue nodes). Integration of ‘omic’ datasets (middle panel), for example transcriptomic, phosphoproteomic or GWAS data, can result in the identification of putative functional modules. By analysing network topology using mathematical tools (right panel), it is possible to identify protein nodes that have a central network position, for example high connectivity or high betweenness, and relating this back to other biological features can provide insights into the systems‐level organisation of cells or organisms. Often several of these approaches are combined.